Traditional electronic medical record information (“EMR”) systems are optimized for storing historical and current records of individual patients or members. Such systems are transaction oriented and patient centered. The data that these systems capture can be mined to provide intelligence that drives improvements in quality of care for individuals and populations, but the readily accessible information from these systems is generally limited to information on individual physicians who treat patients. Moreover, the time required to pull data from large EMR systems to generate operational intelligence across an enterprise is substantial, limiting its utility for supporting ongoing daily decision making. Direct interface with the native EMR database (which is often a non-relational database) is not practical for advanced analytic purposes, especially for a moderate to large size health care delivery enterprise. For example, in an integrated health care delivery enterprise serving 500,000 active members, the associated EMR system or systems might store on the order of several terabytes of data. Therefore, existing data warehousing systems that import from such large systems for comprehensive process analysis have generally been limited to refreshing data weekly or daily.
Embodiments of the present invention arise in this context.